Synthetic Cinnamides and Cinnamates: Antimicrobial Activity, Mechanism of Action, and In Silico Study

The severity of infectious diseases associated with the resistance of microorganisms to drugs highlights the importance of investigating bioactive compounds with antimicrobial potential. Therefore, nineteen synthetic cinnamides and cinnamates having a cinnamoyl nucleus were prepared and submitted for the evaluation of antimicrobial activity against pathogenic fungi and bacteria in this study. To determine the minimum inhibitory concentration (MIC) of the compounds, possible mechanisms of antifungal action, and synergistic effects, microdilution testing in broth was used. The structures of the synthesized products were characterized with FTIR spectroscopy, 1 H-NMR, 13 C-NMR, and HRMS. Derivative 6 presented the best antifungal profile, suggesting that the presence of the butyl substituent potentiates its biological response (MIC = 626.62 μM), followed by compound 4 (672.83 μM) and compound 3 (726.36 μM). All three compounds were fungicidal, with MFC/MIC ≤ 4. For mechanism of action, compounds 4 and 6 directly interacted with the ergosterol present in the fungal plasmatic membrane and with the cell wall. Compound 18 presented the best antibacterial profile (MIC = 458.15 μM), followed by compound 9 (550.96 μM) and compound 6 (626.62 μM), which suggested that the presence of an isopropyl group is important for antibacterial activity. The compounds were bactericidal, with MBC/MIC ≤ 4. Association tests were performed using the Checkerboard method to evaluate potential synergistic effects with nystatin (fungi) and amoxicillin (bacteria). Derivatives 6 and 18 presented additive effects. Molecular docking simulations suggested that the most likely targets of compound 6 in C. albicans were caHOS2 and caRPD3, while the most likely target of compound 18 in S. aureus was saFABH. Our results suggest that these compounds could be used as prototypes to obtain new antimicrobial drugs.

Infectious diseases caused by bacteria and fungi remain a major global health problem, especially in developing countries. Antibacterial and antifungal drugs, widely known as antimicrobials, began to be used in chemotherapy in the 1940s, and many new classes of antimicrobials were discovered in the period from 1940 to 1970 and successfully introduced into clinical practice [67]. However, as these new drugs were employed, drugresistant strains began to appear [68]. The widespread use of antibiotics during the last forty years in animal feed and human consumption has only accelerated the emergence of resistance in various pathogenic organisms [69]. Infection with drug-resistant strains is typically associated with longer treatments, greater toxicity, and higher costs.
The CA skeleton is an interesting scaffold for the development of novel bioactive substances. CA derivatives are known for their antimicrobial activity [21]. Antimicrobial mechanism studies have reported that CA mainly exerts its activity through plasma membrane disruption, nucleic acid and protein damage, and the induction of intracellular reactive oxygen species [70,71]. The biological activities of CA can be explained through its interactions with the molecular targets present in a living organism. CA has three main reactive sites: the aromatic ring substituent, the carboxylic acid function, and the conjugated olefin (Figure 1) [72].
Therefore, the objectives of this study were to synthesize a series of cinnamic acid derivatives and to evaluate their antifungal and antibacterial activities to identify new antimicrobial drug candidates. An in silico approach was used to investigate the potential mechanisms of antimicrobial action.
Infectious diseases caused by bacteria and fungi remain a major global health problem, especially in developing countries. Antibacterial and antifungal drugs, widely known as antimicrobials, began to be used in chemotherapy in the 1940s, and many new classes of antimicrobials were discovered in the period from 1940 to 1970 and successfully introduced into clinical practice [67]. However, as these new drugs were employed, drug-resistant strains began to appear [68]. The widespread use of antibiotics during the last forty years in animal feed and human consumption has only accelerated the emergence of resistance in various pathogenic organisms [69]. Infection with drug-resistant strains is typically associated with longer treatments, greater toxicity, and higher costs.
The CA skeleton is an interesting scaffold for the development of novel bioactive substances. CA derivatives are known for their antimicrobial activity [21]. Antimicrobial mechanism studies have reported that CA mainly exerts its activity through plasma membrane disruption, nucleic acid and protein damage, and the induction of intracellular reactive oxygen species [70,71]. The biological activities of CA can be explained through its interactions with the molecular targets present in a living organism. CA has three main reactive sites: the aromatic ring substituent, the carboxylic acid function, and the conjugated olefin ( Figure 1) [72].
Therefore, the objectives of this study were to synthesize a series of cinnamic acid derivatives and to evaluate their antifungal and antibacterial activities to identify new antimicrobial drug candidates. An in silico approach was used to investigate the potential mechanisms of antimicrobial action.
In the 13 C NMR spectra, chemical shifts indicated that the cinnamic derivatives had nine carbons in common. A signal close to δC 166.0 ppm was attributed to carbonyl; a signal around δC 141.3 ppm was attributed to olefinic carbon. A signal at δC 133.0 ppm was attributed to the aromatic carbon adjacent to the olefinic group. In addition, the signals at about δC 129.6 ppm and δC 128.8 ppm were attributed to meta carbons of the aro- An analysis of the 1 H NMR spectra of the cinnamic derivatives showed seven hydrogens in common in the cinnamoyl substructure, five belonging to the aromatic ring and two olefinic hydrogens referring to the side carbon chain. In view of the common signals for all analogues, the most unprotected hydrogen signal in the spectrum was that of the olefinic hydrogen in the form of a doublet at about δ H 7.60 ppm, which was coupled to the Molecules 2023, 28, 1918 4 of 23 neighboring hydrogen that presented a signal in the form of doublet around δ H 6.53 ppm, evidencing the configuration of the double bond is trans.
Molecules 2023, 28, x FOR PEER REVIEW 3 of 23 droxybenzyl alcohol (12), 4-nitrobenzyl alcohol (13), 4-chlorobenzyl chloride (14), piperonyl alcohol (15), benzyl amine (16), 4-chlorobenzyl amine (17), 4-isopropylbenzyl amine (18), piperonyl amine (19) and dibenzyl amine (20) (Figure 2). The compounds were structurally characterized using infrared (IR) spectroscopy, nuclear magnetic resonance (NMR), and high-resolution mass spectrometry.  In the 13 C NMR spectra, chemical shifts indicated that the cinnamic derivatives had nine carbons in common. A signal close to δ C 166.0 ppm was attributed to carbonyl; a signal around δ C 141.3 ppm was attributed to olefinic carbon. A signal at δ C 133.0 ppm was attributed to the aromatic carbon adjacent to the olefinic group. In addition, the signals at about δ C 129.6 ppm and δ C 128.8 ppm were attributed to meta carbons of the aromatic ring and to two ortho carbons, respectively, and another chemical shift of approximately δ C 127.8 ppm indicated carbon in the para position. Furthermore, the presence of a signal close to δ C 120.3 ppm was attributed to olefinic carbon. The IR spectra showed absorption bands between 2850 and 3000 cm −1 , indicating C-H sp3 stretching; signals between 3000 and 3100 cm −1 indicating C-H sp2 stretching; and C=O stretching bands in the range of 1750-1730 cm −1 characteristic of ester carbonyls or a strong signal around 1650 cm −1 belonging to the carbonyl stretch of the amides.

Evaluation of Antifungal Activity
Following the antifungal activity analysis of the nineteen cinnamic acid analogs against strains of C. albicans (ATCC-76485), C. tropicalis (ATCC-13803), C. glabrata (ATCC-90030), A. flavus (LM-171), and P. citrinum (ATCC-4001) ( Table 1), it was verified that eleven were bioactive. Butyl cinnamate (6) was the most potent compound (MIC = 626.62 µM) against all tested strains. Ethyl cinnamate (3) and methyl cinnamate (2) were also bioactive, though with a lower potency, with MIC = 726.36 µM and 789.19 µM, respectively. In general, the ester derivatives were more bioactive than the amide derivatives. One may analyze whether a substance is fungicidal or fungistatic by calculating its MFC to MIC ratio [76]. Therefore, MFC/MIC ratios were calculated to determine whether the substances presented fungistatic (MFC/MIC > 4) or fungicidal (MFC/MIC ≤ 4) activity. The MFC/MIC ratios were all less than 4 (Table 2), strongly suggesting that the compounds maintain antifungal activity with a fungicidal character.

Mechanism of Action
Sterols make up part of the constitution of all fungal cells. Ergosterol is a principal sterol and modulates membrane fluidity, cell growth, and proliferation [76]. In this study, tests to detect the biological targets of compounds 4 and 6 were performed by adding ergosterol to the medium. An increase in the MIC of the compounds was observed, indicating that the fungus likely acts by inhibiting ergosterol synthesis or directly binding to ergosterol. Azoles and polyenes are well-known classes of antifungal drugs that act on ergosterol to treat fungal infections [76] (Tables 3 and 4). Table 3. Effects of compounds 3 and 6 in the presence or absence of a protective osmotic (sorbitol 0.8 M), as well as in the presence or absence of ergosterol, against the strains of C. albicans. When studying cell wall architecture, protoplasts stabilized with osmo-protectants are important biochemical tools [76]. Osmotic stability is used with C. albicans and other fungi to study antibiotic mechanisms of action [76][77][78]. Damage to essential components of the cell wall, caused by antifungal agents such as cell wall synthesis inhibitors, can lyse cells in the absence of an osmo-protectant. However, cells will continue to grow if an adequate stabilizer is present in the medium. In the sorbitol (osmotic protector) test, the MIC of compounds 3 and 6 increased with microbial growth, indicating interference in cellular functions that are involved the participation of the cell wall (Tables 3 and 4).

Association Tests-Checkerboard Method
In the microdilution assays, compound 6 demonstrated the best antifungal activity, so it was tested in combination with nystatin using the Checkerboard method to analyze its synergistic effect. When tested alone, the compound presented an MIC of 626.62 µM and nystatin presented an MIC of 8.0 µM. When tested in combination, compound 6 presented an MIC of 313.31 µM and nystatin presented an MIC of 3.2 µM, thus demonstrating an additive effect (0.5 < FIC ≤ 1); see Table 5 [79].

Evaluation of the Antibacterial Activity of the Derivatives
According to the results shown in Table 6, when observing the antibacterial activity of the compounds against strains of S. aureus (ATCC-35903), S. epidermidis (ATCC-12228), and P. aeruginosa (ATCC-25853), it was verified that of the 20 tested compounds, 9 were bioactive. 4-isopropylbenzylcinnamide (18) was the most potent (MIC = 458.15 µM), followed by decyl cinnamate (9), which for all tested strains presented an MIC = 550.96 µM. Benzyl cinnamate (10) was equipotent (MIC = 537.81 µM) to compound 9 against S. aureus ATCC-35903 and S. epidermidis ATCC-12228, and it was less active (1075.63 µM) against P. aeruginosa ATCC-25853.  Antibacterial substances can be classified as bacteriostatic or bactericidal. This can be established by calculating the MBC/MIC ratio. A bactericidal effect is considered when the MBC/MIC ratio is ≤4, and a bacteriostatic effect is considered when the ratio is >4 [76]. In the present study, it was possible to determine that the compounds with better results against bacterial strains were also characterized as bactericidal (Table 7).

Association Test Using the Checkerboard Method
The best microdilution test results were obtained with compound 18 (for bacterial strains). This compound was then tested in combination with amoxicillin (an antimicrobial of first choice in the treatment of infections caused by S. aureus) to analyze its synergistic effect using the Checkerboard method.
When tested alone, the compound presented an MIC of 128.0 µg/mL. When tested alone, amoxicillin presented an MIC of 0.015 µg/mL. When tested in combination, the compound presented an MIC of 64.0 µg/mL and amoxicillin presented an MIC of 0.0005 µg/mL, demonstrating an additive effect (0.5 < FICI ≤ 1); see Table 8 [79].

Molecular Docking
Potential targets identified during the target docking predictions for compounds 6 and 18 in C. albicans and S. aureus, respectively, are reported in Table 9. The table contains the UniProt accessions for all predicted potential targets, the ID given to each target, the compound corresponding to each target, a brief functional description, and a column indicating whether the protein structure was retrieved from the Protein Data Bank database or obtained via homology modeling. In total, 18 potential targets were identified for compound 6 in C. albicans and 10 were predicted for compound 18 in S. aureus. In both cases, the potential targets of the chemicals covered a wide range of functions. As described in the Methods section, the compounds were docked into the proteins listed in Table 9. The full results of the molecular docking calculations are given as Supporting Information in Table S1, and the scores of the top scored ligand conformers by target are presented in Table 10. For caTIM10, we were unable to obtain a complete homology model, and no valid docking pose was produced for caCDC42. However, three different docking conditions were explored for caTMP1: compound 6 in the absence of dUMP and 5,10-methylene tetrahydrofolate, docking in the dUMP-binding site in the presence of the cofactor, and docking in the 5,10-methylene tetrahydrofolate cavity with dUMP present.
The docking process produced 62 complexes fulfilling the selection criterion for additional analyses (aggregated Z-score larger than 1). Overall, these calculations showed that the best scored targets of compound 6 in C. albicans were RPD3, HOS2, and IMA1. The best docking scores in S. aureus were obtained for the complexes of compound 18 with FABH, MENB, and GAR. Despite being a widely used tool for computer-aided drug discovery, molecular docking tools employ very simple scoring functions. For this reason, all 62 selected complexes were subjected to MD simulations, and estimations of their free binding energies were performed based on these simulations. The use of MD simulations to refine molecular docking results has been shown to provide better estimates of free energy in ligand-receptor complex binding [80]. Of the predicted complexes, caUGA1 and caUGA11 were excluded from the MD simulation analyses because we could not find Amber parameters for the Fe 2 S 2 cofactor coordinated by the aspartic acid residues present in these receptors. The MD simulations led to a total simulation time of 1.14 µs for all complexes. The predicted free binding energies and their components for the 57 investigated complexes are provided as Supporting Information in Table S2. The results for the best (lowest) energy conformer by target are presented in Figure 3. The results of the free binding energy predictions agreed with those obtained from molecular docking predictions regarding the best ranked molecular target in each microorganism. That is, the most probable targets of compound 6 in C. albicans were caHOS2 and caRPD3, while saFABH was the most likely target of 18 in S. aureus. Notably, for both microbes, the free binding energies derived from the MD simulations were able to separate the top ranked targets better than molecular docking. Based on these results, the predicted binding modes for compound 6 to caHOS2 and 18 to saFABH were analyzed in detail. The 6-caRPD3 complex was excluded from the analysis because all residues in the binding cavity of this receptor and caHOS2 were conserved. In addition, the orientation of the ligand in the predicted complex with caRPD3 overlapped with that predicted for caHOS2. Molecules 2023, 28, x FOR PEER REVIEW 11 of 23  Figure 4 shows the complexes predicted for compound 6 with caHOS2 and compound 18 with saFABH. For depiction, the 100 ligand conformations present in the same number of MD snapshots employed for MM-PBSA calculations were clustered. The centroid of the most populated cluster was selected as the representative conformation of the complex. The residues labeled in Figure 4 are those interacting with the ligands in at least 50% of the analyzed MD snapshots. The figure was prepared using UCSF Chimera [81], the frequencies of the compound-receptor interactions were analyzed with Cytoscape [82], and ligand-receptor interaction diagrams were obtained using LigPlot+ [83].   Figure 4 are those interacting with the ligands in at least 50% of the analyzed MD snapshots. The figure was prepared using UCSF Chimera [81], the frequencies of the compound-receptor interactions were analyzed with Cytoscape [82], and ligand-receptor interaction diagrams were obtained using LigPlot+ [83].

Discussion
Cinnamic acid (1) presented no activity against the studied strains. Compared with its derivative compound 2 (methyl cinnamate), it was observed that alteration of the carboxylic group to an ester function resulted in a bioactive derivative with an MIC of 789.19 μM against all tested Candida strains and an MIC of 1578.16 μM against A. flavus and P. citrinum.

Discussion
Cinnamic acid (1) presented no activity against the studied strains. Compared with its derivative compound 2 (methyl cinnamate), it was observed that alteration of the carboxylic group to an ester function resulted in a bioactive derivative with an MIC of 789.19 µM against all tested Candida strains and an MIC of 1578.16 µM against A. flavus and P. citrinum.
The presence of the ethyl group in compound 3 potentiated its pharmacological response (MIC = 726.36 µM), possibly due to an increase in lipophilicity and greater penetration of the compound into biological membranes. Indeed, the increase in carbon chain length increased the antifungal action of compound 6 (butyl cinnamate, MIC = 626.62 µM). There are several studies and lipophilicity has been extensively studied and is considered one of the most important parameters influencing antifungal activity.
Reduced biological activity (MIC = 1075.63 µM and 2151.26 µM, respectively) was noticed in compound 10 (benzyl cinnamate) compared with compound 6. The introduction of an aromatic ring as a radical, with or without substituents, resulted in a loss of antifungal activity, except for compounds 17 and 18. The molecular volume of these groups may have influenced this change in biological potency.
Of To better understand the mechanism of the antifungal action of the derivatives, compounds 4 and 6 were subjected to a test to verify the mode of action on the cell wall and plasmatic membrane of the fungus using strains of C. albicans ATCC-76485 and P. citrinum ATCC-4001. The test results (Tables 3 and 4) showed that there was a direct interaction between the molecules and ergosterol, a component of the fungus cell membrane, and with sorbitol, an osmotic protector of the cell wall.
Antibacterial activity increased with the length of the carbonic radical chain in compound 2 (methyl cinnamate) with an MIC = 789.19 µM, in compound 3 (ethyl cinnamate) with an MIC = 726.36 µM, and in compounds 4 (propyl cinnamate), 6 (butyl cinnamate), and 9 (decyl cinnamate) with MIC = 672.83 µM, 672.83 µM, and 550.96 µM, respectively, against all studied bacteria. These results suggest an increase in the liposolubility-augmented passage through the biological membrane. compound 7 (pentyl cinnamate) with five carbons in the main chain was inactive against S. aureus ATCC-35903. compound 5 (with an isopropyl group) was bioactive against all tested bacterial strains; however, compound 8 (with an isopentyl group) was inactive, confirming that a greater carbon chain length with a bulky terminal group can result in steric effects that influence bioactivity. In the aryl derivatives group, only compound 18 (4-isopropylbenzylcinnamide) was bioactive against the studied strains, with the best antibacterial profile of the entire collection (MIC = 458.15 µM). The presence of the isopropyl group attached to the aromatic ring was important for antibacterial activity of this compound. In the Checkerboard test, the compounds with the best results (6 and 18) both showed an additive effect when associated with the reference drug.
Subsequently, the antifungal and antibacterial activity of compounds 6 and 18 were investigated through a molecular modeling study. For caHOS2, as well as for caRPD3, the carbonyl oxygen in compound 6 directly points to the Zn 2+ ion. Furthermore, the phenyl ring of the ligand was found to be positioned in parallel with F233 and F177 at the entrance of the binding cavity and to allow for π-π stacking with these residues. This moiety also interacts with D126. These stacking interactions, as well as the strong electrostatic interaction predicted with the metal ion at the active site, are likely to make the largest contributions towards complex stability. In addition, the central enoate group presents contacts with H167, H168, and H205. However, the butyl tail was found to occupy the bottom of the binding cavity, comprising a mostly hydrophobic tunnel flanked by M56, G165, C178, G328, and G329.
In the complex predicted for compound 18 with saFABH, the 4-isopropylbenzyl group occupies the entrance of the binding site and forms a large network of interactions that includes F157, M201, V206, H238, A240, I244, N268, and F298. Two hydrogen bonds contributing to the stability of the complex were predicted: carbonyl oxygen serves as an acceptor for the backbone of G300 and amide nitrogen acts as a donor to the F298 backbone. Finally, the other side of the compound accommodates a hydrophobic pocket formed by L119, L142 I145, T146, F157, and F400 of the second saFABH monomer. This phenyl ring orients favorable π-π stacking interactions with the latter two phenylalanine residues. These modeling results could explain both the antifungal activity of compound 6 and the antibacterial activity of compound 18. Furthermore, caHOS2 has been shown to be a regulator of the essential Hsp90 protein [84]. In antifungal agents, HOS2 inhibition has been demonstrated against C. albicans, and it can even overcome resistance to azole drugs in this fungus [85]. Our results suggest that HOS2 and other histone deacetylases (such as RPD3) are potential antifungal drug targets [86][87][88]. Additionally, FABH is an essential enzyme for the synthesis of fatty acids in bacteria, and it has been investigated for its potential as a drug target [89]. In the specific case of S. aureus, FABH has been validated as a target of many chemical compounds presenting antibacterial activity [90][91][92][93].
Altogether, these modeling predictions were consistent with the antifungal and antibacterial activities observed for compounds 6 and 18, respectively. Although target-specific experiments are required to test these hypotheses, the presented results will be useful in guiding future investigations of the mechanism of action of these compounds.

Conclusions
Nineteen compounds derived from cinnamic acid (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) were synthesized, and eight of them presented antimicrobial activity. Compounds 4 and 6 exhibited the best antifungal results, and compounds 9 and 18 presented the best antibacterial results. Compounds 6 and 18 presented an additive effect with differing antibiotics. Compounds 3 and 6 promoted antifungal activity, both acting on the cell membrane (sorbitol assay) and on the fungal cell wall (ergosterol assay). In the Checkerboard association test of the most bioactive compounds, 6 and 18 presented an additive effect towards inhibitory action. According to the computational results, the most likely targets of compound 6 in C. albicans were caHOS2 and caRPD3, while saFABH was the most likely target of compound 18 in S. aureus. For caHOS2, and for caRPD3, the carbonyl oxygen of compound 6 was found to directly point to the Zn 2+ ion. Furthermore, the phenyl ring of the ligand was shown to be positioned parallel to F233 and F177 at the entrance of the binding cavity, thus allowing for π-π stacking with these residues. The central enoate group made contact with H167, H168, and H205, and the butyl tail occupied the bottom of the binding cavity, comprising a mostly hydrophobic tunnel flanked by M56, G165, C178, G328, and G329. In the complex predicted for compound 18 with saFABH, the 4-isopropylbenzyl group was shown to occupy the entry of the binding site and to form a large network of interactions that included F157, M201, V206, H238, A240, I244, N268, and F298. Two hydrogen bonds that contribute to the stability of the complex were predicted: carbonyl oxygen serves as an acceptor for the G300 backbone and amide nitrogen acts as a donor for the F298 backbone. compound 18 settles into a hydrophobic pocket formed by L119, L142, I145, T146, F157, and F400 of the second saFABH monomer. This phenyl ring is favorable for π-π stacking interactions with the last two phenylalanine residues. Our data indicate that these compounds can be used as prototypes to develop structural analogs with better antimicrobial profiles.

Synthesis of Compounds 2-8
To a 100 mL flask, cinnamic acid (0.25 g, 1.69 mmol) and alcohol (50 mL) were added in the presence of sulfuric acid (0.4 mL); this mixture was then heated under reflux until the completion of the reaction (5-24 h), which was verified with single-spot TLC [51]. Spectroscopic data for the compounds in this study are available in the Supplementary Materials.

Synthesis of compounds 9 and 14
A mixture of cinnamic acid (0.2 g, 1.35 mmol), triethylamine (0.73 mL), and halide (1.39 mmol) in acetone (16.4 mL) was heated under reflux until a complete reaction (24 h), which was verified with single-spot TLC [51]. Spectroscopic data for the compounds in this study are available in the Supplementary Materials.

Synthesis of Compounds 10-13 and 15-20
A mixture of cinnamoyl chloride (0.1 g, 0.6 mmol) and the corresponding alcohol or amine (0.6 mmol) in pyridine (1.0 mL) was heated under reflux until a complete reaction (3-24 h), which was verified with single-spot TLC [94]. Spectroscopic data for the compounds in this study are available in the Supplementary Materials.

Microorganisms
The 20 selected compounds were checked for antifungal activity against strains of C. albicans (ATCC-76485), C. tropicalis (ATCC-13803), C. glabrata (ATCC-90030), A. flavus (LM-171), and P. citrinum (ATCC-4001), and their antibacterial activity was evaluated against strains of S. aureus (ATCC-35903), S. epidermidis (ATCC-12228), and P. aeruginosa (ATCC-25853). The strains were acquired at the MICOTECA of the Mycology Laboratory, Department of Pharmaceutical Sciences (DCF), Health Science Center (CCS) of the Federal University of Paraíba. All strains were kept on Sabouraud dextrose agar (SDA) and in brain heart infusion broth (BHI) at a temperature of 4 • C, and they were used for the assays at 24-48 h in SDA/BHIA while incubated at 35 ± 2 • C. The microorganism suspension was prepared according to the 0.5 McFarland standard to obtain 1-5 × 10 6 CFU/mL. Standard drugs, nystatin (yeast) and amoxicillin, were used as controls.

Determination of Minimum Inhibitory Concentration (MIC)
MIC values were determined using the broth microdilution method and 96-well U-shaped plates. In each well, 100 µL of liquid medium from Roswell Park Memorial Institute (RPMI) doubly concentrated with 100 mL of product solution was added to the first row of plate wells. Through serial dilutions, concentrations of the evaluated compounds ranging from 1000 µg/mL to 2.0 µg/mL. Subsequently, 10 µL of inoculum was added to the wells in each column of the plate and the culture medium with nystatin. The plates were then incubated at 37 • C for 24-48 h. For each strain, the MIC was defined as the lowest concentration capable of inhibiting fungal growth in the wells, visually observed in comparison with the control. All tests were performed in duplicate, and the results are expressed as an arithmetic mean of the MIC values obtained in both tests [95].

Determination of Minimum Fungicidal Concentration (MFC)
After the MIC values were determined, 10 µL aliquots of the supernatant from the wells in which complete fungal growth inhibition (MIC, MIC × 2, and MIC × 4) was observed in the microdilution plates were added to 100 µL of RPMI broth contained in new culture plates. The plates were incubated for 24-48 h at 35 ± 2 • C. The minimum fungicidal concentration (MFC) was considered as the lowest concentration of the product that was able to inhibit the growth of microorganisms [96].

Determination of Minimum Bactericidal Concentration (MBC)
After the MIC values were determined, 10 µL aliquots of the supernatants were removed from the wells of the microdilution plates at concentrations of MIC, MIC × 2, and MIC × 4 for each strain and inoculated into new microdilution plates containing only a BHI medium. The assay was performed in triplicate. The plates were incubated at 35 ± 2 • C for 24 h, and bacterial growth was observed. The MBC was defined as the lowest concentration capable of causing the complete inhibition of bacterial growth [96,97].

Mechanism of Antifungal Action for Amides
(1) Sorbitol Assay: The microdilution technique was performed in the presence of sorbitol (anhydrous D-sorbitol) (INLAB laboratory) to determine the mode of action of the compounds on the cell wall of C. albicans CBS 562. For this test, the inoculum was prepared with sorbitol to a final concentration of 0.8 M. The plates were incubated, and readings were taken at 24 h and 48 h post-incubation. Caspofungin was used as a positive control at an initial concentration of 5 mg/mL [76,98].
(2) Ergosterol Test: This test was performed using the microdilution technique, as previously described, in the presence of exogenous ergosterol at 400 µg/mL. Nystatin was used as a positive control. The plates were incubated, and readings were taken at 24 and 48 h [76,98].

Association Study Using the Checkerboard Method
In a 96-well plate, all the wells were filled with 100 µL of SD broth. Compounds 6 and 18 were initially dissolved in DMSO and further diluted in SD broth Then, 100 µL of the compound was added to the initial column (wells A1 to H1) and serially diluted up to the 10th column. The 11th and 12th columns, respectively, served as a drug-only control and a growth control. Similarly, a drug control was initially dissolved in DMSO and diluted in broth to obtain a 4 × concentration of the desired drug concentration. It was separately prepared in each tube for the desired concentration. One hundred microliters of the first control drug concentration was added to the first row A wells A1 to A11 but not to A12 (the growth control). This process was repeated with the respective drug concentrations for rows B to G (but not for row H). All wells were thoroughly mixed by pipette aspiration to obtain proper drug dispersion. One hundred microliters (100 µL) of the contents of each well was transferred to another 96-well plate, which was marked as a replica plate. Finally, 100 µL of the inoculum (1 × 10 3 to 3 × 10 3 CFU/mL) was added to all wells, and the plates were incubated at 37 • C for 7 days [79].

Target Selection
The previously described homology-based target fishing approach was employed to identify potential targets for compounds 6 and 18, respectively, in C. albicans and S. aureus [74,98]. Potential targets for each compound were separately predicted using the Similarity Ensemble Approach (SEA) web server [99]. For each compound, the sequences of the targets provided by SEA were retrieved from the Uniprot database. The sequences of the targets predicted for compound 6 were then submitted to a Blast [100] search using the proteins from C. albicans (taxid 5476) included in the Reference Protein (refseq_protein) database using the NCBI web implementation of Blast [101]. The same process was repeated for compound 18 using proteins from S. aureus (taxid 1280). Two criteria were used to select the potential targets: the selected proteins had to have a shared identity of at least 45% with the proteins identified during target fishing, and at least of 75% of their sequences had to be covered by the alignment obtained from the Blast search. Proteins fulfilling the latter criteria were selected for modeling studies.

Molecular Docking
An initial three-dimensional (3D) conformation was generated for each compound with OpenEye's Omega [102], and partial atomic charges of the am1bcc type were added to these with MolCharge [103]. The 3D structures of a few targets were available in the Protein Data Bank (database). These targets were ACPS (PDB code 5cxd), MEMB (PDB code 2uzf), FABH (PDB code 6kvs), and GBSA (PDB code 5eyu) from S. aureus and TDH3 (PDB code 7u4s) from C. albicans. The remaining targets predicted for compounds 6 and 18 had no available 3D structure, and homology models were generated for them on the SwissModel web server [104]. Various homology models were generated for each protein, and the highest QMEANDisCo global score by potential target was selected for molecular docking calculations.
For molecular docking, the previously reported procedure was followed [74,105]. The Gold software [106] was selected for docking calculations. The binding cavities were defined from either the co-crystallized ligands or the ligands present in the templates of the homology models. Functionally relevant cofactors or metal ions were maintained in the receptor in case they were already present or were transferred to it from the model's templates. Hydrogen atoms were added to the receptors, and residues pointing to the binding cavity were set to be flexible. The PLP scoring function was selected for primary scoring. A total of 30 docking solutions were obtained and re-scored with the ChemScore, GoldScore, and ASP GoldScore scoring functions.
Considering the average and standard deviations of the scoring values, the scores obtained with each of the four scoring functions were converted to Z-scores at a scoring function level. The resulting Z-scores were then averaged. The ligand poses with aggregated Z-scores >1 were selected for additional studies. These selected potential complexes were further studied with molecular dynamics (MD) simulations, and free binding energies were predicted from the MD snapshots as described below.

Molecular Dynamics Simulations and Free Energy Calculations
MD simulations were carried out with Amber 20 [107] according to previously described methods [1,76]. The same protocol was applied to model all systems, parameterizing the ligands and amino acids with the gaff2 and ff19SB force fields, respectively. For cofactors, Amber parameters were obtained from the database provided by the Bryce Group at The University of Manchester: http://amber.manchester.ac.uk/index.html (accessed on 26 May 2022). Cofactors not included in the latter database such as dUMP and 5,10-methylene tetrahydrofolate were parameterized using the same approach followed for ligands. Parameters for the Zn 2+ ion, as well as for its coordinating residues, were retrieved from the Yuan-Ping Pang lab web page: https://www.mayo.edu/research/labs/computer-aided-molecular-design/ projects/zinc-protein-simulations-using-cationic-dummy-atom-cada-approach (accessed on 26 May 2022) [108].
Truncated octahedron boxes were generated to enclose the systems that were solvated with OPC water molecules. Systems were neutralized with the addition of Na + and Cl − ions at a concentration of 0.15 M [109]. Two stages of energy minimization were performed, the first with restraints applied to everything except the solvent and counter ions and the second with no restraints. The systems were next heated from 0 K to 300 K (the temperature during the 20 ps production runs). The heated systems were subsequently equilibrated at constant temperature and pressure for 100 ps. The final system for each complex was used as input for five production runs, each initialized with differing random velocities to better explore each complex's conformational space. Each production run lasted for 4 ns, totaling 20 ns per complex.
The MM-PBSA method, as implemented in the MMPBSA.py script of Amber 20, was used to estimate the free binding energies. Calculations proceeded with 100 MD snapshots evenly extracted from five production runs. To compute the free binding energies, 20 snapshots per production run were extracted. Only snapshots in the 1 ns to 4 ns time interval were considered for MM-PBSA calculations. The ionic strength for these calculations was set at 150 mM.